Generalized out-of-distribution detection and beyond in vision language model era: A survey

A Miyai, J Yang, J Zhang, Y Ming, Y Lin, Q Yu… - arXiv preprint arXiv …, 2024 - arxiv.org
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …

Anomalydiffusion: Few-shot anomaly image generation with diffusion model

T Hu, J Zhang, R Yi, Y Du, X Chen, L Liu… - Proceedings of the …, 2024 - ojs.aaai.org
Anomaly inspection plays an important role in industrial manufacture. Existing anomaly
inspection methods are limited in their performance due to insufficient anomaly data …

A diffusion-based framework for multi-class anomaly detection

H He, J Zhang, H Chen, X Chen, Z Li, X Chen… - Proceedings of the …, 2024 - ojs.aaai.org
Reconstruction-based approaches have achieved remarkable outcomes in anomaly
detection. The exceptional image reconstruction capabilities of recently popular diffusion …

Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection

C Wang, W Zhu, BB Gao, Z Gan… - Proceedings of the …, 2024 - openaccess.thecvf.com
Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid
development. However the recent development of IAD approach has encountered certain …

Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection

Y Cao, J Zhang, L Frittoli, Y Cheng, W Shen… - … on Computer Vision, 2025 - Springer
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images
from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging …

Exploring plain vit reconstruction for multi-class unsupervised anomaly detection

J Zhang, X Chen, Y Wang, C Wang, Y Liu, X Li… - arXiv preprint arXiv …, 2023 - arxiv.org
This work studies the recently proposed challenging and practical Multi-class Unsupervised
Anomaly Detection (MUAD) task, which only requires normal images for training while …

Weakly supervised video anomaly detection and localization with spatio-temporal prompts

P Wu, X Zhou, G Pang, Z Yang, Q Yan… - Proceedings of the …, 2024 - dl.acm.org
Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-
level anomalous event detection with only coarse video-level annotations available. Existing …

A Survey on Safe Multi-Modal Learning Systems

T Zhao, L Zhang, Y Ma, L Cheng - Proceedings of the 30th ACM SIGKDD …, 2024 - dl.acm.org
In the rapidly evolving landscape of artificial intelligence, multimodal learning systems
(MMLS) have gained traction for their ability to process and integrate information from …

Large language models for anomaly and out-of-distribution detection: A survey

R Xu, K Ding - arXiv preprint arXiv:2409.01980, 2024 - arxiv.org
Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the
reliability and trustworthiness of machine learning systems. Recently, Large Language …

Rethinking Reverse Distillation for Multi-Modal Anomaly Detection

Z Gu, J Zhang, L Liu, X Chen, J Peng, Z Gan… - Proceedings of the …, 2024 - ojs.aaai.org
In recent years, there has been significant progress in employing color images for anomaly
detection in industrial scenarios, but it is insufficient for identifying anomalies that are …